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IJSTR >> Volume 6 - Issue 10, October 2017 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Comparative Analysis Of The Performance Of Principal Component Analysis (PCA) And Linear Discriminant Analysis (LDA) As Face Recognition Techniques

[Full Text]

 

AUTHOR(S)

Frank Peprah, Michael Asante

 

KEYWORDS

(Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Euclidean Distance Metric, City Block Metric, Cosine Metric, Eigenface,Generalization ability)

 

ABSTRACT

Face Recognition System employs a variety of feature extraction (projection) techniques which are grouped into Appearance-Based and Feature-Based. In a vast majority of the studies undertaken in the field of Face Recognition special attention is given to the Appearance-Based Methods which represent the dominant and most popular feature extraction technique used. Even though a number of comparative studies exist, researchers have not reached consensus within the scientific community regarding the relative ranking of the efficiency of the appearance-based methods (LDA, PCA etc) for face recognition task. This paper studied two appearance-based methods (LDA, PCA) separately with three (3) distance metrics (similarity measures) such as Euclidean distance, City Block & Cosine to ascertain which projection-metric combination was relatively more efficient in terms of time it takes to recognise a face. The study considered the effect of varying the image data size in a training database on all the projection-metric methods implemented. LDA-Cosine Distance Metric was consequently ascertained to be the most efficient when tested with two separate standard databases (AT & T Face Database and Indian Face Database). It was also concluded that LDA outperformed PCA.

 

REFERENCES

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